Synovial fluid IL-1β appears useful for the diagnosis of chronic periprosthetic joint infection
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the role of synovial fluid interleukin (IL)-1β in diagnosing chronic periprosthetic joint infection (PJI) and to identify the optimal threshold of synovial fluid IL-1β for differentiating chronic PJI from aseptic failure after knee and hip arthroplasties. METHODS: Between January 2019 and December 2019, we prospectively included patients scheduled to have a revision surgery for chronic PJI or aseptic failure after total joint arthroplasty. Then, synovial IL-1β was additionally measured along with routine preoperative diagnostic serum and synovial biomarkers. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were analyzed for each biomarker to determine diagnostic efficacy. RESULTS: Of the 93 patients included, their demographic data were not found to be statistically significant. The median synovial IL-1β levels were significantly higher in the chronic PJI group than in the aseptic group (894.73 pg/mL vs. 34.49 pg/mL, P<0.01). The AUC for synovial fluid IL-1β was 0.991, which was higher than serum ESR (0.627) and CRP (0.712). The optimal threshold value for detecting chronic PJI of synovial IL-1β was 312.7 pg/mL, with a sensitivity of 97.3% and a specificity of 94.64%. And the combined measurement of synovial fluid IL-1β and synovial fluid PMN% can led to a specificity of 1, and a negative predictive value (NPV) of 1. CONCLUSIONS: The present study demonstrated that synovial fluid IL-1β is a valuable biomarker for detection of chronic PJI. The combination of synovial fluid IL-1β and PMN% led to an improvement in specificity compared with evaluation of each single index. TRIAL REGISTRATION: This study was prospectively registered on the Chinese Clinical Trial Registry (a non-profit organization, established according to both the WHO International Clinical Trials Register Platform Standard and Ottawa Group Standard), and the registering number was ChiCTR1800020440 . Registered on December 29, 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".